Speed Control of BLDC Motor Based on Recurrent Wavelet Neural Network

Abstract

In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.

Wavelet Neural Networks for Speed Control of BLDC Motor

“…Recurrent networks have feedback and are also known as feedback networks. There are several types of recurrent networks that depend on the feedback connection [12][13][14][15][16][17][18][19][20][21][22].…”

“…The WNN-PID controller based on PSO is proposed in this section, which combines the ability of the artificial neural networks for learning with the ability of wavelet for identification, control of dynamic system, and also having the capability of self-learning and adapting [10,11,19,37]. Two types of wavelet network are modified in this section, feedforward WNN and proposed recurrent WNN with online tuning optimization using PSO algorithm [22][23][24].…”

“…One hidden layer with four neurons is used. Three types of mother wavelet functions are used and they are: the Mexican hat function is [10,11,22,37,39] ψ…”

Anti‐windup scheme based on 2DOF‐PIλDμcontroller for velocity tracking of linear induction motor

“…Each particle performance is determined by employing a cost function, which differs relying on the optimization issue. 14,17,18 For a multidimensional issue, the position and velocity of such a single particle in the swarm are obtained by exploiting the following equations:…”

Motion control of linear induction motor based on optimal recurrent wavelet neural network-PID controller

“…Where the weights of the network along with the translation and dilation of the wavelets function are optimized using some learning algorithm to get adaptive WNN. Wavelet neural networks (WNNs) can be categorized into feedforward (non-recurrent) and recurrent categories [7], [8].…”